High performance computing (HPC) and “big data” analytics generally involve the use of multiple servers in a datacenter to compute data faster and more efficiently than possible with standalone computing devices. However, typical “big data” and other high-throughput applications often require intimate knowledge of the execution environment, fine-tuning, and/or a static environment. Generally, workloads consisting of many jobs/tasks are provisioned to various computing resources (e.g., rack-based servers), which may be homogenous computing environments consisting, for example, only a particular class of servers or may be heterogeneous computing environments consisting of any number of different classes (e.g., different architectures and/or server generations) of servers. In some systems, virtualization and/or cloud abstraction layers may present heterogeneous infrastructures as homogeneous infrastructures but, in doing so, may exact significant allocation inefficiencies.